dataset stretch now optional
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@@ -39,15 +39,18 @@ class ResidualConvClassifier(BinaryMaskDatasetMixin,
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self.conv_list.append(ConvModule(last_shape, self.conv_filters[0], (k, k), conv_stride=(2, 2), conv_padding=1,
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**self.params.module_kwargs))
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last_shape = self.conv_list[-1].shape
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for filters in self.conv_filters:
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conv_module_params.update(conv_filters=filters)
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for idx in range(len(self.conv_filters)):
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conv_module_params.update(conv_filters=self.conv_filters[idx])
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self.conv_list.append(ResidualModule(last_shape, ConvModule, 3, **conv_module_params))
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last_shape = self.conv_list[-1].shape
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self.conv_list.append(ConvModule(last_shape, filters, (k, k), conv_stride=(2, 2), conv_padding=2,
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**self.params.module_kwargs))
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for param in self.conv_list[-1].parameters():
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param.requires_grad = False
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last_shape = self.conv_list[-1].shape
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try:
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self.conv_list.append(ConvModule(last_shape, self.conv_filters[idx+1], (k, k), conv_stride=(2, 2), conv_padding=2,
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**self.params.module_kwargs))
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for param in self.conv_list[-1].parameters():
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param.requires_grad = False
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last_shape = self.conv_list[-1].shape
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except IndexError:
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pass
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self.full_1 = LinearModule(self.conv_list[-1].shape, self.params.lat_dim, **self.params.module_kwargs)
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self.full_2 = LinearModule(self.full_1.shape, self.full_1.shape * 2, **self.params.module_kwargs)
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